ArticleAging2024
Sphingolipids in prostate cancer prognosis: integrating single-cell and bulk sequencing.
Article in Aging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Multimode MALDI-MSI deciphers matrine-induced metabolic reprogramming in prostate cancer xenografts: spatial mapping of low-molecular-weight compound alterations.Frontiers in pharmacology · 2025Article
- The Role of Sphingolipid Metabolism in Pregnancy-Associated Breast Cancer After Chemotherapy.Biomedicines · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStratifying patient risk and exploring the tumor microenvironment are critical endeavors in prostate cancer research, essential for advancing our understanding and management of this disease.
methodsSingle-cell sequencing data for prostate cancer were sourced from the pradcellatlas website, while bulk transcriptome data were obtained from the TCGA database. Dimensionality reduction cluster analysis was employed to investigate heterogeneity in single-cell sequencing data. Gene set enrichment analysis, utilizing GO and KEGG pathways, was conducted to explore functional aspects. Weighted gene coexpression network analysis (WGCNA) identified key gene modules. Prognostic models were developed using Cox regression and LASSO regression techniques, implemented in R software. Validation of key gene expression levels was performed via PCR assays.
resultsThrough integrative analysis of single-cell and bulk transcriptome data, key genes implicated in prostate cancer pathogenesis were identified. A prognostic model focused on sphingolipid metabolism (SRSR) was constructed, comprising five genes: "FUS," "MARK3," "CHTOP," "ILF3," and "ARIH2." This model effectively stratified patients into high-risk and low-risk groups, with the high-risk cohort exhibiting significantly poorer prognoses. Furthermore, distinct differences in the immune microenvironment were observed between these groups. Validation of key gene expression, exemplified by ILF3, was confirmed through PCR analysis.
conclusionThis study contributes to our understanding of the role of sphingolipid metabolism in prostate cancer diagnosis and treatment. The identified prognostic model holds promise for improving risk stratification and patient outcomes in clinical settings.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.